the humour and bullying project: modelling cross-lagged and dyadic data

The Humour and bullying project: Modelling cross-lagged and dyadic data.

Dr Simon C. HunterSchool of Psychological Sciences and Health,

University of Strathclydeemail: [email protected]

This research was funded by the Economic and Social Research Council, award reference RES-062-23-2647.

Project team and info

P.I.: Dr Claire Fox, Keele UniversityResearch Fellow: Dr Siân JonesProject team: Sirandou Saidy Khan, Hayley Gilman, Katie Walker, Katie Wright-Bevans, Lucy James, Rebecca Hale, Rebecca Serella, Toni Karic, Mary-Louise Corr, Claire Wilson, and Victoria Caines.

Thanks and acknowledgements:Teachers, parents and children in the participating schools.The ESRC.

More info:Project blog: http://esrcbullyingandhumourproject.wordpress.com/Twitter: @Humour_Bullying

• Background and rationale to the Humour & Bullying project• Methods used in the project• AMOS – what is it, why use it (and pertinently, why not)?• Measurement models – achieving fit. • Cross-lagged analyses• Dyadic data – issues and analyses• Summary

Overview

Humour

Social: Strengthening relationships, but also excluding, humiliating, or manipulating others (Martin, 2007).

Personal: To cope with dis/stress, esp. in reappraisal and in ‘replacing’ negative feelings (Martin, 2007).

What functions does humour serve?

Both of these functions are directly relevant to bullying and peer-victimisation contexts.

Multi-dimensional (Fox et al., in press; Martin, 2007):• Self-enhancing: Not detrimental toward others (e.g. ‘I find that

laughing and joking are good ways to cope with problems’).

• Aggressive: Enhancing the self at the expense of others (e.g. ‘If someone makes a mistake I often tease them about it’).

• Affiliative: Enhances relationships and can reduce interpersonal tensions (e.g. ‘I often make people laugh by telling jokes or funny stories’).

• Self-defeating: Enhances relationships, but at the expense of personal integrity or one’s own emotional needs (e.g. ‘I often put myself down when making jokes or trying to be funny’).

Humour

Peer-victimisation• Repeated attacks on an individual.• Conceptualised as a continuum rather than a category.

Also multi-dimensional in nature:• Verbal: Being teased or called names.• Physical: Hitting, kicking etc. Also

includes property damage.• Social: Exclusion, rumour spreading.

• Clearly a stressful experience for many young people, associated with depressive symptomatology (Hunter et al., 2007, 2010),

anxiety (Visconti et al., 2010), self-harm (Viljoen et al., 2005) and suicidal ideation (Dempsey et al., 2011; Kaltiala-Heino et al., 2009), PTSD (Idsoe et al.,

2012; Tehrani et al., 2004), loneliness (Catterson & Hunter, 2010; Woodhouse et al.,

2012), psychosomatic problems (Gini & Pozzoli, 2009), ...etc• Also a very social experience – peer roles can be broader than

just victim or aggressor (Salmivalli et al., 1996).

Peer-victimisation

Peer-victimisation

Klein and Kuiper (2008): • Children who are bullied have much less opportunity to interact

with their peers and so are at a disadvantage with respect to the development of humour competence.Cross-sectional data support: Peer-victimisation is negatively

correlated with both affiliative and self-enhancing humour (Fox & Lyford, 2009).

May be particularly true for victims of social aggression.

Victims gravitate toward more use of self-defeating humour?May be particularly true for victims of verbal aggression as

peers directly supply the victim with negative self-relevant cognitions such as “You’re a loser”, “You’re stupid” etc which are internalised (see also Rose & Abramson, 1992, re. depressive cognitions).

Primarily, to evaluate the relationship between humour use, involvement in bullying (as victim or aggressor), and adjustment Testing causal hypotheses, e.g., verbal victimisation will cause

an increase in levels of self-defeating humour (Rose & Abramson, 1992)

Evaluating proposed explanatory causal pathways, e.g. the effect of victimisation on mental health is mediated via negative humour styles

Evaluating humour as a risk / resilience factor, e.g. high levels of positive humour may buffer young people against the negative effects of peer-victimisation

Assessing whether friendships serve as a contextual risk factor for peer-victimisation

Research aims

Methods

• N=1241 (612 male), 11-13 years old, from six Secondary schools in England.

• Data collected at two points in time: At the start and at the end of the 2011-2012 school session.

• Data collection spread over two sessions at each time point due to number of tasks.

• N=807 present at all four data collection sessions.

Measures

Self-Report:• 24-item Child Humour Styles Questionnaire (Fox et al., in press).

• 10-item Children’s Depression Inventory – Short Form (Kovacs, 1985).

• 36-item Victimisation and Aggression (Owens et al., 2005).

• 10-item Self-esteem (Rosenberg, 1965).• 4-item Loneliness (Asher et al., 1984; Rotenberg et

al., 2005).

Measures

Peer-nomination:• Rated liking of all peers (Asher & Dodge, 1986).• Nominated friends and very best friend (Parker & Asher, 1993).• Peer-victimisation and use of aggression (Björkqvist et al., 1992),

participants nominated up to three peers for Verbal, Physical, and Indirect. This was an 8-item measure. Verbal: “Gets called nasty names by other children.” Physical: “Gets kicked, hit and pushed around by other children.” Indirect – “Gets left out of the group by other children” and “Has nasty

rumours spread about them by other children.”

• Humour (adapted from Fox et al., in press). This was a 4-item measure, young people nominated up to three peers for each item.

• Easy to use graphical interface, comes as an SPSS bolt-on.• Can use for path analysis, assessment of measurement models,

and structural equation modeling.• Includes features such as bootstrapping, modification indices,

assessment of multivariate normality etc. But… these can’t be used if you have missing data, on

relevant items, in your SPSS data file!

AMOS

Measurement models describe the relationships between indicators and latent variables.

Latent variable

Manifest indicators

Error

Measurement models

Depression

Item 1 Item 2 Item 3

error1 error2 error3

Measurement models may have more complex structures.

Measurement models



Verbal Victimisation



PhysicalVictimisation

Measurement models







General Victimisation

Resid2Resid1

Measurement models may fit well when you model them in a straightforward manner in AMOS (like those just shown).

If they don’t fit well, there are different ways to try and improve fit.

Measurement models

Measurement models







Correlate error terms:

Measurement modelsModel the method variance:

Item 1[R] Item 2 Item 3

[R]


Item 4 Item 5[R] Item 6


Depression

Method

Measurement modelsCreate ‘parcels’ (Bandalos, 2002; Little et al., 2002) so that this…





Depression

Measurement models…becomes this

Parcel 1 Parcel 2

error1 error3

Parcel 3

error5

Depression

• Most likely to do this if you have a factor which has lots of items.

• Need unidimensional construct: Parcelling is problematic if you are unsure of the factor structure. Little et al. (2002) suggest this is the biggest threat in terms of model misspecification. Clearly, cannot be used when the goal of your analysis is to

understand fully the relations among items

• Some measures actually come with instructions to parcel (e.g. the Control, Agency, and Means–Ends Interview: Little et al., 1995)

Parcelling

• May improve fit because fewer parameters are being estimated (so better sample size to variable ratio). This can therefore also be a way to deal with smaller sample sizes when you have measures with lots of indicators. But, likely to improve fit across all models regardless of

whether they are correctly specified – increasing the chances that we will fail to reject a model which should be rejected (Type II error)

Parcelling

Cross-sectional data are restricted in terms of unpacking causation relating to two correlated variables. Three explanations:

Cross-lagged analyses

• Both variables influence the other• One variable influences the other

VerbalVictimisation

• Omitted variable accounts for correlation

Self-DefeatingHumour

Symptoms ofDepression

Cross-lagged data, sometimes called panel data, allow us to move forward in our understanding of how the variables influence each other.• ‘A happened, followed by B’ design• More complex to analyse than cross-sectional data

Critique• Omitted variable bias still a problem.• Only looks at group level change, not individual level (where

latent growth curve models might be more appropriate)



T1Victimisation

T2Depression

T2Victimisation

T1Depression

e

e

The cross-lagged model (also referred to as a simplex model, an autoregressive model, a conditional model, or a transition model).

Can the history of victimisation predict depression, taking into consideration the history of depression (and vice-versa)?


T1Victimisation

T2Depression

T2Victimisation

T1Depression

e

e

Can also include time-invariant predictors in the model.

Gender


NB – the model can be extended to incorporate further time points.

T1Victimisation

T2Depression

T2Victimisation

T1Depression

e

e

T3Depression

T3Victimisation

e

e

Analysis 1: Victimisation and Internalising

Internalising = withdrawal, anxiety, fearfulness, and depression (Rapport et al., 2001). Operationalised here as symptoms of depression and loneliness.


A stability-only model• Stability paths

Competing models:

T1 PhysicalVictimisation


T1Internalising

T1 VerbalVictimisation


T1 SocialVictimisation


T2Internalising

A stability and a restricted cross-lagged model• Stability paths• PLUS cross-lagged within related concept only (victimisation)

Competing models:







T1Internalising

T2Internalising

A fully cross-lagged model• Stability paths, cross-lagged within related concept • Cross-lagged across all variables

Competing models:







T1Internalising

T2Internalising

Fit indices• Chi-square: ideally, non-significant.• CMIN/DF: under 2 or 3.• CFI: > .95 = good, >.90 = adequate.• RMSEA: < .050 = good, < .080 = adequate.

Results:• Model comparisons: All models significantly different from each

other (ΔX2, p < .001).


Results:


X2 CMIN/DF CFI RMSEA

Model 1 105.98 (df = 23), p < .001

4.608 .987 .054 (.044, .065)

Model 2 57.51 (df = 17),p < .001

3.383 .994 .044 (.032, .057)

Model 3 10.24 (df = 11), p = .509

0.931 1.000 .000 (.000, .028)

Cross-sectional results: • Different forms of peer-victimisation all positively associated with

each other (T1 = .69 to .78; T2 = .57 to .69).

• Different forms of peer-victimisation all positively associated with internalising. Verbal = .54 (T2 = .39), Physical = .46 (T2 = .23), Social = .59 (T2 = .43). Social and verbal seem most problematic


Cross-Lagged Results (only significant paths shown)








.11

-.14

.12

.10

.15.17

.13

.57

.47

.37

.36

T1Internalising

T2Internalising

Analysis 2: Victimisation, Humour, and Internalising


Results:• Model comparisons: All models were again significantly different

from each other (ΔX2, p < .001).


X2 CMIN/DF CFI RMSEA

Model 1 283.54 (df = 83), p < .001

3.416 .977 .044 (.039, .050)

Model 2 175.54 (df = 63),p < .001

2.786 .987 .038(.031, .045)

Model 3 36.58 (df = 27), p = .103

1.355 .999 .017 (.000, .030)

Cross-sectional associations: T1 (T2)


2. Agg 3. SD 4. SE 5. Verb 6. Phys 7. Soc 8. Int

1. Aff Hum .13 (.06) -.16 (-.22) .35 (.26) -.15 (-.12) -.14 (-.10) -.16 (-.17) -.38 (-.31)

2. Agg Hum - .14 (.19) .04 (-.04) .07 (.04) .09 (.07) .03 (.01) -.05 (.02)

3. SD Hum - .09 (.01) .38 (.28) .31 (.17) .34 (.23) .49 (.48)

4. SE Hum - -.06 (-.04) -.04 (-.04) -.07 (-.06) -.20 (-.25)

5. Verbal - .73 (.65) .78 (.69) .53 (.38)

6. Physical - .69 (.57) .46 (.23)

7. Social - .59 (.43)

8. Interlsng -


SocialVictimisation

VerbalVictimisation

-.13

.13

.09 .12

.15

.12

Self-Enhancing Humour

Affiliative Humour

Aggressive Humour

Self-Defeating Humour


SocialVictimisation

VerbalVictimisation

Self-Enhancing Humour

Affiliative Humour

Aggressive Humour

Self-Defeating Humour

Time 1 Time 2

.13

.08

.07

.07

-.14

.20 -.17-.14

.11

T1Internalising

T2Internalising


Method summary• Useful for beginning to disentangle cause and effect• Not a panacea – still has limitations

Results summary• Humour may be self-reinforcing (virtuous cycle…of sorts)• (Mal)adjustment appears to be an important driver of both

humour use and peer-victimisation Implications for intervention re. adolescent mental health and

adolescent attitudes toward those with mental health issues

Dyadic analyses - APIM

Children in friendships are likely to produce data which are related in some way, violating the normal assumption that all data are independent. Usually, we just ignore this.

Shared experiences may shape friends’ similarity or their similarity may be what the attraction was to begin with.

The Actor-Partner Independence Model (APIM: Kenny,

1996) makes a virtue of this data structure.

Can conduct APIM analyses in SEM, or using MLM (where individual scores are seen as nested within groups with an n of 2)

Dyadic analyses

T1Victimisation

Friend’s T2Victimisation

T2Victimisation


e

e

Looks a lot like the cross-lagged analysis

However, the data structure in SPSS is very different.

Dyadic analyses

Standard data entry in SPSS:

ParticipantT1 ParticipantT2

Person 1 Data Data

Person 2 Data Data

Person 3 Data Data

Person 4 Data Data

Dyadic analyses

For APIM, data is entered by dyad, not person:

ParticipantT1 FriendT1 ParticipantT2 FriendT2

Dyad 1 Data Data Data Data




Dyadic analyses

We can evaluate “actor effects”

T1Victimisation


T2Victimisation


e

e

Dyadic analyses

We can evaluate “partner effects”

T1Victimisation


T2Victimisation


e

e

Dyadic analyses

We can evaluate the simple correlation between partners

T1Victimisation


T2Victimisation


e

e

Dyadic analyses

An important issue is the nature of the dyads you have, either indistinguishable or distinguishable.

Analyses differ according to the type of dyad you have.

Dyadic analyses

Indistinguishable dyads: Best friends.

ParticipantT1 FriendT1 ParticipantT2 FriendT2





Dyadic analyses

Distinguishable dyads: Victim and Aggressor.

VictimT1 AgrsorT1 VictimT2 AgrsorT2





Dyadic analyses

Indistinguishable dyads

Within-person effects must be constrained to be equal

T1Victimisation


T2Victimisation


e

e

Dyadic analyses


Partner effects must be constrained to be equal

T1Victimisation


T2Victimisation


e

e

Dyadic analyses


Intercepts for the Time 2 variables must be constrained to be equal

T1Victimisation


T2Victimisation


e

e

Dyadic analyses


The means and variances of both Time 1 variables must be constrained to be equal

T1Victimisation


T2Victimisation


e

e

Dyadic analyses


The variances of the residual variances must be equal

T1Victimisation


T2Victimisation


e

e

Dyadic analyses

Victim’s Depression

T1

Victim’s Friend’sDepression T1

Victim’s Depression

T2

Victim’s Friend’sDepression T1

e

e

Distinguishable dyads: e.g. Victim with Non-Victimised Best Friend

We implement none of the preceding constraints.

We can now have more complex patterns: e.g. victim’s level of depression may predict non-victim’s depression but not vice-versa.

Dyadic analyses

• ‘Couple’ pattern (actor and partner effects are equal and of the same sign).

• ‘Contrast’ pattern (actor and partner effects are equal but have opposite signs).

• ‘Actor-only’ pattern (an actor effect but no partner effect)• ‘Partner-only’ pattern (a partner effect but no actor effect)

Kenny & Ledermann (2010) recommend calculating a parameter they introduce called k to quantitatively assess what kind of patternn you have:

To calculate k, you use ‘phantom variables’(not going into this today)

Dyadic analyses

Humour & Bullying Data

Indistinguishable dyads: Best friends. • Ndyad = 457 (best friends at T1)

First APIM:• Depressive symptomatology of best friend dyads.

Dyadic analyses

This is a saturated model, therefore there is ‘perfect fit’

Dyadic analyses

Depression

T2 Friend’s Depression

T2 Depression

Friend’s Depression

.59

.59

.12

.12

Actor effect is significant: .59, p < .001Partner effect is also significant: .12, p < .001

Dyadic analyses

Second APIM:• Depressive Symptomatology of Best Friend Dyads.• Victimisation of Best Friend Dyads

Earlier analyses suggested that mental health drove victimisation. True for friend effects too?

Dyadic analyses

Cross-sectional ACTOR (& PARTNER) correlations at T1


1. Depression 2. Social 3. Verbal 4. Physical

1. Depression N/A (.18) .44 (.09) .40 (.10) .36 (.06)

2. Social N/A (.12) .80 (.12) .68 (.05)

3. Verbal N/A (.16) .70 (.12)

4. Physical N/A (.14)

• Strong actor correlations between constructs, all positive• Partner correlations between social/verbal victimisation and

depression, and between verbal and physical victimisation


Friend’s PhysicalVictimisation

Friend’s SocialVictimisation

Social Victimisation


Friend’s Verbal Victimisation


Depression




SocialVictimisation




Depression

Time 1 Time 2

.34

.34

.11

.11

.08

.08

-.12

-.12

.58

.58

.28

.28

.52

.52

.29

.29








Depression




SocialVictimisation




Depression

Time 1 Time 2

.08

.34

.34

.24

.24

.22

.22

.08

.58

.58








Depression




SocialVictimisation




Depression

Time 1 Time 2

.23

.23

.12

.12

.16

.16

.34

.34.28

.28








Depression




SocialVictimisation




DepressionTime 1 Time 2

.16

.16

-.35

-.35

-.12

-.25

-.25

.24 .24

-.19 -.19

.52

.52-.12








Depression




SocialVictimisation




Depression

Time 1 Time 2

.11

.11

-.16

-.16

.29

.29

Dyadic analyses

Summary• Friendships as context for development of worsening victimisation

(except for social victimisation)• Verbal victimisation seems to be most problematic for friends of

victims, not those experiencing it• Conversely, social victimisation seems to be ‘good’ for friends of

victims• Physical victimisation has fewer effects

• Can be... challenging to report results!

Summary

AMOS• Easy to use, accessible, though can be limited• Trouble shooting fairly straightforward

Cross-lagged analyses• Good way to address limitations of cross-sectional designs• Pretty straightforward to analyse, even with a number of variables• Still has limitations

APIM analyses• Can reveal very different picture to cross-lagged analyses• Can be quite complex to build model in AMOS, especially when

multiple variables included

Bandalos, D.L. (2002). The effect of item parceling on goodness-of-fit and parameter estimate bias in structural equation modeling. Structural Equation Modeling, 9, 78-102.

Berrington, A. (2006). An overview of methods for the analysis of panel data. ESRC National Centre for Research Methods Briefing Paper / 007.

Cook, W.L., & Kenny, D.A. (2005). The Actor-Independence Model: A model of bidirectional effects in developmental studies. International Journal of Behavioral Development, 29, 101-109.

Farrell, A.D. (1994). Structural equation modeling with longitudinal data: Strategies for examining group differences and reciprocal relationships. Journal of Consulting and Clinical Psychology, 62, 477-487.

Holt, J.K. (2004). Item parceling in structural equation models for optimum solutions. Paper presented at the Annual Meeting of the Mid-Western Educational Research Association, October 13 – 16, Columbus, OH.

Kenny, D.A. (1996). Models of non-independence in dyadic research. Journal of Social and Personal Relationships, 13, 279.

Lederman, T., Macho, S., & Kenny, D.A. (N/A) Assessing mediation in dyadic data using APIM. [powerpoint slides]

Little, T.D., Cunningham, W.A., Shahar, G., & Widaman, K.F. (2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9, 151-173.

Further reading

http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1066&context=edpsychpapers




http://eprints.ncrm.ac.uk/415/1/MethodsReviewPaperNCRM-007.pdf

https://mywebspace.wisc.edu/rlbrown3/web/dyad/Cookandkenny.pdf

http://www.google.co.uk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CCcQFjAA&url=http://131.156.49.11/ETRA/Presentation/Faculty/Item%20Parceling%20paper.doc&ei=1neKUOfyNeKx0QXJ8ICgDg&usg=AFQjCNFiKk1zTp6DjFZyvH5SDZqvoH451w&sig2=AHLbqudYZRAwxCt0xeJNgw



http://www.modeling.uconn.edu/handouts/D1S3_Lederman_APIMeM_UConn_2011.pdf

http://faculty.psy.ohio-state.edu/cunningham/pdf/little.sem.2002.pdf

the humour and bullying project: modelling cross-lagged and dyadic data

Documents

humour bullying projectmethods

modelling cross

project blog

dr claire fox

social experience peer

dr simon

edinburgh university

social research council